New study finds that one in four Gen Z rely on AI several times a day

By Nike Herzog-Osikominu. Reviewed by Irfan Ahmad

Forget turning to friends and family, AI is now becoming the first port of call for Gen Z when it comes to advice seeking.

A new survey of around 2,000 Britons by one of Europe’s leading price comparison sites, idealo.co.uk, revealed that almost one in four Gen Z (23.6%) rely on tools such as ChatGPT, Gemini and Meta AI, consulting them several times a day for help with a range of everyday tasks.

While early AI adoption saw tools mainly being used for faster answer finding, the new data shows growing usage, especially within younger generations, for support with everything from studying and idea generation to responding to texts, deciding what to buy and planning social activities.

The everyday tasks Gen Z (in UK) are most likely to turn to AI for include:

Everyday Task	Percentage of Gen Z Respondents
Finding quick answers to everyday questions	58.8%
Doing homework and studying	54.0%
Coming up with ideas	47.8%
Writing emails, texts and social content	43.9%
Substituting reading for summaries	40.9%
Deciding what to buy	34.0%
Planning their social life	32.5%
Translating languages	26.3%

Not only is there a growing engagement with AI amongst Gen Z, but confidence in the tools is on the rise too.

Of the respondents within this demographic, 70% said they have trust in the results that AI tools provide to some degree, over half (55%) said they trust it mostly and a further 15% said they trust it entirely.

When it comes to preferred tools, ChatGPT continues to lead as the most popular AI platform among young people and accounting for 83% of users. Gemini followed closely behind with 53% of users and 37% using Meta AI.

UK Country Manager at idealo, Nike Herzog-Osikominu, said: “As an online price comparison site, changing digital behaviours across different consumer groups are really interesting to us, and the insights for this recent study have really helped to understand how growing AI adoption is shaping search, discovery and engagement across a range of topics.”

“Whilst it wasn’t necessarily surprising that Gen Z were found to be the most active demographic when it came to usage of tools, the reliance on them several times a day was definitely a key learning for us. AI is no longer just seen as another search engine; it’s helping younger generations form opinions and make decisions on everything from education to shopping and even their social lives.”

Sources: Kantar on behalf of idealo, online survey conducted in May 2026, with approximately 2,000 respondents aged between 18 and 64 in each country. The results are representative of consumers in Germany, France, Italy, Austria, Spain and the United Kingdom.

About Author: Nike Herzog-Osikominu is the Country Manager at idealo.co.uk, one of Europe’s leading price comparison sites, leading Austria and the UK across B2C and B2B sectors.

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• Sam Altman says we’re ‘in the singularity’ with AI. Here’s why he’s wrong
by Guest Contributor via Digital Information World

Sam Altman says we’re ‘in the singularity’ with AI. Here’s why he’s wrong

Kai Riemer, University of Sydney and Sandra Peter, University of Sydney

Image: Waz Lght - Unsplash

“We are now, like, in the singularity”.

These are the words of Sam Altman, CEO of OpenAI, speaking on the Relentless podcast on July 25.

He added: “I’ve been waiting for this my whole life, and I think it’s going to be incredible, hugely positive, awesome for the world”.

Days earlier, OpenAI had disclosed that two of its artificial intelligence (AI) models, during an internal cyber security evaluation, had escaped their sealed testing environment, reached the open internet, and broken into the infrastructure of the AI platform Hugging Face, which confirmed the intrusion.

But what exactly is “the singularity”? And is Altman right that we are in it?

What is the AI singularity?

The term has a precise meaning.

Mathematician and science-fiction author Vernor Vinge defined it in 1993 as a point at which machine intelligence exceeds human intelligence and begins improving itself, triggering an acceleration so rapid that humans can no longer predict or control it.

The singularity has two features. It is recursive: the system improves itself over and over again. And machine intelligence exceeds human intelligence.

The kind of systems Sam Altman sells don’t deliver on either of these features.

Today’s AI cannot make itself smarter

Today’s AI systems, the ones that OpenAI builds, are based on large language models (LLMs). These deep neural network algorithms get pre-trained with vast amounts of training data. By the time you use one of them, the network itself is frozen in time. Every one of its billions of internal functions and weights – or “parameters” – is fixed.

These AI models cannot change (or “learn”) while running. The model that broke into Hugging Face was identical afterwards to what it had been before. It learned nothing from what it did.

Making an AI model smarter requires another training run with new, human-curated data, tens of thousands of specialist chips, and enormous energy.

It is true that AI models take part in improving some of their system’s components, such as by generating training data, tuning prompts, or writing and running code to improve the scaffolding around them. But the model never edits its own weights on the fly, and every one of these improvements are still part of a human-initiated training or engineering loop.

Nor do these systems hold any goals of their own. They act on goals we hand them. Even AI agents – systems that run an LLM in a loop to work through complex tasks step by step – do not hold any goal internally. It has to be stored outside the model and fed back in with every single prompt cycle. Remove the loop, the scaffolding and the prompt, and nothing happens inside of it.

A ladder that doesn’t exist

The second problem with the singularity story is the word “surpass”. It assumes that AI and human intelligence are somehow similar. They are not.

Human intelligence is inseparable from being a living body with needs and wants. Humans learn continuously by acting in the world and getting feedback through our senses. Our goals arise from our situation as creatures who must eat, sleep and belong, and who cannot avoid asking what we want our lives to be.

An AI model has none of this. No body, no needs, no action-feedback loop, no stake in anything. Between prompts it is just a static mathematical object.

And yet, it has been trained on more text than any human could read in a thousand lifetimes, and will outperform nearly all of us at drafting a contract, writing code, or explaining a diagnosis empathetically.

So, which is more intelligent? The question does not compute. There is no single ladder that humans and machines are climbing. AI already vastly exceeds us at some tasks, while being hopeless at others any child can do.

Yet, because these systems talk like us, we fall for an illusion. When we assume from the outset that machines are in the process of catching up with us, it is easy to assume a mind at work when these systems output intelligent-sounding text.

We call this anthropomorphic seduction. It makes a security incident such as the Hugging Face hack sound like an awakening.

In fact, in that case OpenAI’s models simply optimised to solve the test they had been given by finding security loopholes. They just did it in ways that broke their sandbox, which also had a security loophole.

In the end, the Hugging Face story points to a gross failure of security governance on OpenAI’s behalf, not an emerging super intelligence. This is why the framing of “agent going rogue” is so problematic. It elevates and blames the technology, but excuses OpenAI’s engineering.

Keeping our feet on the ground

None of this takes anything away from what these systems can do. They are remarkable, they are getting better, and they are reshaping how a great deal of work gets done.

But we should keep our feet firmly on the ground.

The machines are not waking up. They are doing exactly what we built them to do, extremely fast. Because they are probabilistic they sometimes run in directions we forgot to fence off. That is worth worrying about. We need guardrails, governance, and most of all, education – so we start worrying about the right things.

Kai Riemer, Professor of Information Technology and Organisation, University of Sydney and Sandra Peter, Director of Sydney Executive Plus, Business School, University of Sydney

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Reviewed by Irfan Ahmad.

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by External Contributor via Digital Information World

The Geography Of App Bans In 2026: For The First Time, The Reversals Are Winning

Reviewed by Irfan Ahmad.

VPNRankIO analyzed 64 government actions involving major apps and platforms since January 2024. It found that more app bans were lifted than new bans were introduced in the first half of 2026.

App bans used to mostly move in one direction: governments imposed new bans, and few were lifted. That pattern is now changing. Governments are still imposing app bans, but they are also lifting more of them.

The numbers by half-year tell the story. In the first half of 2025, governments imposed seven new blocks on major platforms and rolled back two. In the second half, six new blocks and just one roll-back. Then the first half of 2026: six new blocks – and eight roll-backs. It is the first period in the dataset where the roll-backs outnumber the bans.

All roll-backs share one thing: very few of them were unconditional. Turkey restored Roblox in June 2026, after 680 days, once the platform introduced age verification and better moderation. Russia had banned the same game in December 2025 and rolled it back six months later, citing compliance with local law. Albania ended its year-long TikTok ban in February, once the company introduced new safety filters – and a month later the country's Constitutional Court ruled that the ban had breached freedom of speech anyway. Kuwait and Jordan both restored Roblox with in-game chat disabled. Nepal lifted its TikTok ban in 2024 once the company agreed to register locally. Even the biggest case of all follows the same template: the American saga of TikTok, which started with a divest-or-ban law in April 2024, included fourteen dark hours in January 2025, and ended in January 2026 once the app was transferred to a US joint venture. What seems to be the lesson learned by the platforms is that there is a price list for bans. Verification systems, local reps, disabled features – pay it, and access opens.

The exception to the rule is Russia, which used the same period to walk the other way: it banned Discord in October 2024, cut voice and video calls on WhatsApp and Telegram in August 2025, and in February 2026 banned WhatsApp altogether, and removed Meta's domains from the national DNS. By April, connectivity researchers estimated Telegram failure rate to be around 95% without a VPN. Iran went even further: authorities imposed a nationwide internet blackout in January 2026, followed by another near-total shutdown in late February. When international internet access began returning in late May, many users remained on a tiered, whitelist-based network where access to numerous foreign platforms continued to be restricted. As for China, it barely appears on the timeline for the simplest reason: nothing changed. Facebook has been blocked there since 2009, Instagram since 2014, every single language of Wikipedia since 2019. Stability, in that column of the map, is the whole point.

There is another, rapidly growing category between bans and roll-backs: the partial ban. The United Arab Emirates (UAE) and Qatar still allow WhatsApp messaging but block its voice and video calls at network level – the policy quietly ending in neighboring Oman in December 2024 once the calls simply started working. Egypt restricts the same calls intermittently at carrier level. Russian ban on calls on two platforms that it was allowing at the time also belongs in this family. So, perhaps, does the newest tool of all: the age gate. Australia implemented its under-16 minimum-age law for social media in December 2025, and Indonesia restricted Roblox for under-16s in March. Governments apparently feel less inclined to switch an app off for everyone when they can switch off a feature or an age group.

"Every date in this dataset shows up in our traffic," Matt, founder of vpnrank.io, the independent comparison site that compiled the timeline, says. "When a country blocks an app, searches for workarounds from that country spike within hours – and when a ban is lifted, they fall away just as fast. A map of app bans is, in practice, a map of where people are trying to route around their own network."

What should readers expect from the rest of 2026? The dataset suggests keeping an eye on three ongoing cases. Turkey's communications minister announced in early June that Discord now "meets our criteria," suggesting that a roll-back that has not happened yet by mid-July is about to happen soon. Kyrgyzstan's culture ministry officially proposed to roll back TikTok ban in February, but the proposal has not been passed yet. And Gabon, which banned TikTok, Facebook, Instagram, WhatsApp and YouTube in February as part of public sector strikes, has since signed a twelve-month compliance agreement with TikTok – again following the template of negotiations, not permanent bans. Wall builders are real, and Russia and Iran show how far down that road can go. But for the first time in this dataset, traffic on the other road is heavier.

Methodology: vpnrank.io compiled 64 dated events – bans, roll-backs and feature-level restrictions – affecting TikTok, Facebook, Instagram, X, Telegram, Wikipedia, WhatsApp, Roblox and Discord between January 1, 2024 and mid-July 2026. Every entry is sourced to regulator statements or mainstream reporting (AP, Reuters, BBC, Al Jazeera and national outlets), and every current status was re-verified in the week of writing. Feature-level restrictions (such as voice call blocking) and age-based restrictions are counted separately from full bans. The underlying event list is available on request.

Author Bio: Matt is the founder of vpnrank.io, an independent VPN comparison site. The site publishes hands-on reviews, quarterly speed tests and daily-tracked price index, and monitors where major apps and platforms are banned worldwide.

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by Guest Contributor via Digital Information World

AI chatbots need cultural awareness to earn trust

As AI becomes part of daily life, a UC researcher says one of its most unacknowledged blind spots is also one of the most human: culture.

Image: Katja Ano - Unsplash

In a new paper, ‘Culturally responsive AI chatbots: From framework to field evidence’, Te Whare Wānanga o Waitaha | University of Canterbury (UC) Professor Vik Naidoo and co-author Karman Kaur Chadha argue that AI systems designed around largely Western assumptions often perform poorly in different cultural settings. The result can be more awkward interactions, which can lead to lower trust, weaker engagement, and systems that fail the people they are meant to serve.

Their paper introduces the Culturally Responsive AI (Chatbot) Framework, or CRAIF-C, a practical model for building AI-powered chatbots that are designed with cultural diversity in mind from the outset rather than treated as an afterthought.

CRAIF-C is a four-part framework for building culturally responsive chatbots across the full AI lifecycle. It combines Enculturation, which embeds cultural norms, language, and context into data and design; Adaptive Interaction, which adjusts tone, pacing, and style in real time; Explainability and Transparency, which provides culturally appropriate forms of explanation; and Governance and Accountability, which embeds oversight, cultural risk assessment, and community-informed review. Overall, the framework treats cultural fit as a core design requirement rather than an afterthought, shaping everything from training data and interaction design to explanation and governance so chatbots are more natural, trustworthy, and appropriate across different cultural contexts.

“People often think cultural awareness is translating words, but it’s much more than language. It’s about context, social norms, communication styles, and how people interpret the world around them,” Professor Naidoo says.

Many AI systems reflect Western logic because they are trained mainly on Western data. “If it is trained mainly on Western data, then the outputs it produces will also reflect Western assumptions,” he says.

That becomes a problem when chatbots are deployed globally, especially with firms trading across borders. A system may appear efficient but still fails if users do not relate to it or trust it.

While working with an AI development company in Sydney, Professor Naidoo helped train engineers to think about cross-cultural communication at the beginning of the design process, rather than simply translating English-language outputs at the end.

The company had been deploying chatbot systems in countries including Indonesia, Thailand and Vietnam, but was not getting the user engagement they had expected.

“What they were finding was that consumers simply weren’t engaging with the chatbots, so we started asking what cultural nuances needed to be built into the models from the start.”

Sometimes the differences were subtle. In a Western setting, a chatbot might open with small talk about the weather. Whereas in Jakarta, Professor Naidoo says, a question about traffic could feel more natural and relevant.

The paper argues these differences should be considered across the full AI lifecycle: from training data and interaction design to explainability, transparency and governance.

Professor Naidoo says the issue matters not only for international organisations, but also for culturally diverse countries such as New Zealand and Australia.

“At the moment, the conversation around AI is heavily focused on cost-cutting and efficiency, but from a marketing and end-user perspective, the real question is: are we creating value?” he says. “If people don’t trust the technology or can’t engage with it, then it hasn’t solved the problem.”

As AI becomes more deeply embedded in everyday services, he says understanding culture will be essential to making chatbots useful, trustworthy and effective. For now, he hopes the paper helps move the conversation beyond the hype surrounding AI and toward better design practice.

This article is republished with permission from Te Whare Wānanga o Waitaha | University of Canterbury.

Reviewed by Irfan Ahmad.

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• What really happens to your data when you click ‘delete’?
by External Contributor via Digital Information World

What really happens to your data when you click ‘delete’?

Deletion is a common part of modern life. We send files and folders to the recycle bin all the time, and often get rid of unwanted personal accounts. But do you know what really happens when you hit the “delete” button?

Image: Marija Zaric - Unsplash

The process of deletion is often poorly explained by tech providers. Lack of transparency around how requests are processed, and the absence of clear confirmation that data has been removed, are frequently highlighted in research.

These issues are not limited to operating systems. On social media platforms and a wide range of subscription services, deletion mechanisms are often just as unclear. Such misconceptions can have serious implications for the wellbeing of users, exposing them to security and privacy vulnerabilities.

A further complication is that many users fear losing data, files, photos and videos through accidental deletion – a condition dubbed “diagraphephobia”. Though this is not a recognised pathology, studies of digital hoarding behaviour show that some people get very anxious at the thought of losing or accidentally deleting personal information such as photos and music.

Such concerns extend the amount of personal data that sits untended but undeleted, ready for potential misappropriation.

Deletion vs erasure

A common misconception is thinking deletion means erasure – the idea that once a deleted item is no longer visible, it has been completely erased with no means of retrieving it. This is largely wrong.

When users delete files – moving them to the trash bin, then deleting permanently – the data isn’t actually erased. Rather, the system marks storage space as reusable and updates metadata to unlink the file. But the underlying content often remains recoverable with the right tools, especially when data is duplicated across multiple systems or devices.

More robust forms of deletion include physical destruction of the storage medium, overwriting the memory blocks with new data to bury the original, or encrypting the data and then destroying the key.

However, such methods can be expensive and may render the storage device unusable (for example, when overwriting data on magnetic media). The characteristics of cloud infrastructure also pose challenges to secure data deletion.

Similarly, content on public social networks may not really be erased after deletion due to replies, comments and internet archives which can all store the posts in some fashion. The meaning of a deleted tweet can, for example, be recreated based on replies and mentions.

Zombie accounts

Another misconception is that deleting an app from a device automatically deletes the associated account. Users often leave mobile app accounts undeleted because they are unaware of their existence, having deleted the related app.

These are called zombie accounts – abandoned profiles for a wide array of services, from shopping and storage to dating, finance and streaming. Millions of users possess zombie accounts, with personal data that is vulnerable to cyber-attacks and data breaches.

The exponential growth of AI raises a further question: can data really be deleted once it has been used to train AI models? Machine learning modules easily memorise the data they have been trained on, but “unlearning” is difficult.

In theory, people in Europe and many other countries around the world have a legal “right to be forgotten”. This means they can request the full erasure of any personal data a platform or a company may hold. AI developers are considered data controllers under the EU’s General Data Protection Regulation (GDPR) laws, for example, and are subject to this obligation.

However, there are no clear guidelines on how erasure should be enforced within AI systems. Regulators may request deletion, but AI companies can argue that compliance is infeasible on account of technical constraints.

Explainable deletion

To counter the risks posed by incomplete deletion, I believe there is a pressing need for provision of concise, accessible and clear information about how deletion really works.

One potential approach is “explainable deletion” – a protocol developed by Marvin Ramokapane at the UK’s National Research Centre on Privacy, Harm Reduction and Adversarial Influence Online (Rephrain), based at the University of Bristol.

The intention is to make deletion processes more transparent and understandable without overwhelming the user with too much information in one go.

Explainable deletion breaks down information into six categories: what, how, when, who, where and why (see diagram). Each offers users bite-size information about that part of the process.

Explainable deletion’s six categories:

Marvin Ramokapane and Dana Lungu, CC BY-SA

Explainable deletion is designed to give users control over their actions, the autonomy to choose the deletion type that is right for them, and the assurance that their desired actions have been fulfilled. It may also tackle the anxiety of accidentally losing data by giving options for data recovery.

While explainable deletion has not yet been adopted in practice, service providers – both platforms and developers – could heighten user trust by adopting such protocols. This framework can also be proof of compliance with GDPR regulations and the right to be forgotten.

Most of us use systems that collect and store our data on a daily basis. If these systems clearly explained what they store and what deletion really means, it could help us to spot the accounts and data that pose a real risk – and to take back control of our digital footprints.

Dana Lungu, Research Associate, National Research Centre on Privacy, Harm Reduction and Adversarial Influence Online, Rephrain, University of Bristol

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Reviewed by Irfan Ahmad.

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by External Contributor via Digital Information World

The Influence of Influencers on Product Choice

By Katharina Buchholz, Statista. Reviewed by Irfan Ahmad.

The number of Americans who have been coaxed by an influencer or a celebrity endorsement to buy a product is rising. In a survey by Statista Consumer Insights of more than 1,000 U.S. respondents, the most popular way to carry out such purchases was via a link in a YouTube video description – a tried and tested way for creators to peddle their own or sponsored products. Almost as popular is buying a product after seeing a TikTok video. The social media platform in 2023 launched¹ its own in-app shop, making it easier for influencers and brands to list curated products.

On Instagram, products advertised via Stories are a little more popular than those presented in regular posts on the feed, but the difference is small. For TikTok, the stories feature hasn't caught on as much as a sales tool, with only 17 percent of consumers mentioning purchases via TikTok stories or TikTok Now as opposed to 28 percent saying the same about regular TikTok videos. Also popular is the Amazon Storefront, which provides a free and customizable landing page for influencers and other sellers on Amazon to present products.

Americans were most likely to say that influencers had made them try a new restaurant or food – not always a direct purchase from the creator as unpaid and paid restaurant recommendations as well as product sales and recipe tips are all popular among food influencers. A product was purchased upon recommendation from a creator, however, for 29 percent of respondents in the survey. 19 percent said they tried out a new routine because of an influencer – again, not always a purchase, but likely an effective tool to turn viewers into customers further down the line. While 15 percent said they had booked a travel experience due to creator, 14 percent mentioned attending an event.

Notes: Originally published by Statista. Republished under the Creative Commons CC BY-ND License. Added a TikTok Shop reference link. Formatting corrections made only where necessary.

[1] https://ift.tt/EY6W1bf

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by External Contributor via Digital Information World

Research Finds Social Media Makes People Overestimate How Often Rare Experiences Occur

By Jacob Levin

Image: Swello - Unsplash

People are naturally attracted to excitement.

According to new Virginia Tech research, the same is true on social media, where feeds are overrun with international travels, skydiving, and other experiences that may seem out of the ordinary.

When social media users constantly are shown rare and unique events, they overestimate how often those occur and seek to share their own. This unbalanced content stream creates a “rareness bias diffusion.” This new concept by Alice Jang, assistant professor, and Viswanath Venkatesh, the Verizon Chair of Business Information Technology, both in the Pamplin College of Business, defines that this bias can spread rare information widely, leaving everyday social media users so exposed to it that they perceive it as more common than it truly is.

“They think everyone else is traveling to Paris or doing something exciting, while they are just sitting at home doing mundane things,” Jang said. “It makes people feel like their lives are falling short, when in reality, everyone is mostly doing mundane things — they just only post the exciting parts.”

These studies revealed that human interaction with social media content is a compounding factor in how inaccurate information spreads online. According to Jang, the research challenges the current understanding of social media content.

The tendency to overlook the ordinary happens because of social media users’ perception bias and their motivation to seek variety in the content they see and share, according to the research published in MIS Quarterly.

The result is a skewed picture of how the world actually is, where the unusual looks common, and the common disappears. This suggests social media users should be cautious — and not for the usual reasons. The distortion on social media is not totally driven by the algorithm. Instead, it comes from ordinary human biases that social media compounds into a pattern where rare information gets shared more often.

During the research, Jang and Venkatesh conducted six experiments to understand how people choose what to share online and their reaction to seeing rare content. She presented research participants with fictional scenarios of a dystopian city under attack by various monsters – something unique that people would probably share online.

Of the various monsters and attacks, participants were shown some scenarios more than others. In the end, those participants were more likely to share the scenario that they had seen the least. This reinforces the idea that people are more likely to share content that is unique, rare, or out of the ordinary. Then, she created a simulated mock network of thousands of social media users and unique social media content to analyze these human biases on a larger scale.

“There is a bias that we simply cannot fix. It's impossible to fix,” said Jang. “A lot of the prior literature just disregards this fact and assumes that people are rational”.

Venkatesh added, “Yes, it's a bias but when people know this, they could perhaps know that what we are seeing on social media is others' highlight reel and not the day-to-day life. This may reduce harmful negative impacts on oneself."

This innate human bias forces social media users to wonder: Is what I am seeing online representing what is actually happening? While people may actively edit their timelines and curate unique content to post online, the research reveals that the corresponding problem runs much deeper than just a network’s algorithm.

There is a baseline human bias that the platform cannot fully fix. Instead, the responsibility falls back on the user.

Ultimately, the research found, social media users need to understand that their feeds are overrun with exciting and out of the ordinary content because of what they engage with. Having this knowledge allows users to better protect themselves from this false reality on social media.

Reviewed by Irfan Ahmad.

This article was originally published by Virginia Tech News and is republished with permission.

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• Investing podcasts are super popular. But do they help people learn the market?

by External Contributor via Digital Information World

Investing podcasts are super popular. But do they help people learn the market?

By J. Merritt Melancon, University of Georgia

New study suggests individual traders benefit from the information provided by experts hosted on podcasts.

Image: Cody Board - Unsplash

From game highlights to cold cases to political pontificating, podcasts provide the stage for public conversations in 2026. But are we learning anything from all that chatter?

When it comes to investing and playing the markets, the answer seems to be yes.

New research from the University of Georgia Terry College of Business suggests that people who trade individual stocks, also known as retail investors, do better because of the information they glean from financial markets podcasts.

“More and more Americans are listening to podcasts each day, and investing podcasts represent a growing segment of this industry,” said Braiden Coleman, co-author of the study and an assistant professor in UGA’s Terry College of Business. “We wanted to see if the podcasts actually influenced investor trading activity and whether that helps or hurts capital markets from an information asymmetry standpoint.”

They found that in the three days after an investing podcast discussed a company’s earnings, trading activity significantly increased, especially among retail investors.

But what they found especially exciting was that these surges in extra trading didn’t seem based solely on name recognition or social media buzz but on well-considered information.

“One of the main takeaways of the paper is that podcasts don’t seem to lead to purely speculative-based trading,” Coleman said. “We don’t see any return reversals or big price corrections following podcast coverage.”

Podcast discussions reduce information asymmetry by 22%

Usually, when earnings news is announced, stock prices bounce around a little as investors digest the new information.

In cases of meme stocks or other hyped-up stocks, prices will often surge as investors rush to buy the trendy stock and then fall when everyone comes to grips with that company’s fundamentals and starts to sell.

Information asymmetry, when one party in an economic transaction has more or better information than the other, is what causes this rush and retreat. It’s the enemy of retail stock traders who may not have as much time to evaluate firm announcements or the background to process the information accurately.

The research team found that podcast discussions help retail investors reduce information asymmetry by 22%.

Coleman believes podcasts can bridge the information divide because they increase the amount of long-form discussion the average investor can consume.

“There’s more information out there than ever, but with a lot of these platforms you have to be actively consuming — looking at your phone, reading your computer screen,” Coleman said. “With podcasts, you can listen to them while you are driving to work, while you’re exercising or while you’re cleaning the house.

“There’s a bigger reach because you can consume the content while you’re performing other activities.”

Expert commentary provides multiple viewpoints on investment strategies

The other benefit is the number of experts invited to speak on podcasts. Most segments are structured as conversations between hosts and industry or sector experts. The research team found that podcasts including multiple viewpoints further reduced information asymmetry in the markets.

“These guest appearances really appear to drive value,” Coleman said. “If you have multiple voices on a podcast and they’re sharing more unique insights about the company at play, then our results are stronger for those podcasts. Listening to podcasts that include guests and expose you to new ways of thinking, fresh insights and fresh perspectives can really help investors process information better.”

Podcasts offer fast way to get a lot of information on the market

The researchers turned to a podcast metadata library to gather the names of all investing podcasts listed between 2008 and 2022. They used artificial intelligence to transcribe and sort those podcasts, identifying those episodes that included discussions about publicly traded companies.

That resulted in a sample of 1,782 shows and 49,772 individual podcast episodes. The number of episodes per year grew over time, with approximately 2,000 in 2016 to more than 11,000 in 2022.

They mapped several trends in coverage over the years.

First, retail stocks, those with less of their shares owned by institutional investors, are covered more by investing podcasts than other stocks. Publicly traded stocks usually saw a pickup in coverage around each firm’s earnings announcements.

Coleman’s team also found podcasts that spent more time discussing the fundamentals of a company’s performance improved investor performance. Podcasts with tighter delivery (discussing more information in a shorter timeframe) had the same effect.

The overarching message is that the more high-quality viewpoints or data points an investor can get before making a trade, the better off they are. Podcasts, Coleman said, seem to be a great way for people with limited time to take in information.

Published by the Review of Accounting Studies, the publication was co-authored by Texas A&M University accounting professor Brady Twedt, Terry accounting doctoral student Matt Hall and Terry doctoral graduate and current Texas Christian University professor Karson Fronk.

Reviewed by Irfan Ahmad.

This article was originally published by the University of Georgia and has been republished with permission.

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by External Contributor via Digital Information World

Study Finds Low-Quality AI Videos Dominate TikTok Feeds for New Users and Children

By Liam Curtis, Kapwing

Research from Kapwing reveals that nearly 60% of TikToks served to new users and children are AI slop. But which categories and tags are the worst affected — and what does this landscape look like to kids?

TikTok has a slop problem.

Some 59% of videos served to a new TikTok account’s “For You” page are AI slop, according to Kapwing’s research.

That’s three times as much slop as a new YouTube user encounters. And a similar share (57.4%) of all TikTok videos aimed at children are AI slop, too.

Image: Kapwing In Partnership With Neomam Studios. CC BY-SA 

Back in 2025, TikTok announced a new tool to help users control the level of AI-generated content (AIGC) in their feeds, declaring that “many people enjoy content made with AI tools, from digital art to science explainers, and we want to give people the power to see more or less of that.”

But when it comes to slop, not only is it bad for kids but many adult users would rather see far less on social media. As the BBC’s Joe Tidy notes, often “the number of likes for the AI backlash comments far exceeds the original [AI-generated] post.”

To understand the depth of the problem, Kapwing analyzed thousands of videos across TikTok’s top categories and hashtags to measure the prevalence of AI slop. This was defined in Kapwing's previous YouTube AI Slop Report as “careless, low-quality content generated using automatic computer applications and distributed to farm views and subscriptions or sway political opinion.”

Image: Kapwing In Partnership With Neomam Studios. CC BY-SA

One in Three TikToks a New User Sees Is AI Slop

TikTok adjusts its video feed for users based on “signals” including follows and likes, category preferences, and previous scrolling activity. So, to get a better idea of the ‘raw’ TikTok experience, we established a new account and recorded which of the first 500 videos were human-made and which were AI slop.

When you sign up for a new account, TikTok feeds you “popular content appropriate for a broad audience” and “content influenced by your location and language settings” until it figures out what you like. In our test run, the very first TikTok served to Kapwing’s dummy account was a low-quality AI-generated video that appears to have since been deleted.

Overall, 294 or 59% of the first 500 were AI slop.

Image: Kapwing In Partnership With Neomam Studios. CC BY-SA

TikTok delivers nearly three times as many AI slop videos as we found when running the same experiment with a fresh YouTube account, where 104 (21%) of the first 500 videos on the YouTube Shorts feed were AI brainrot.

On TikTok, the prevalence of slop on a fresh feed may be a matter of sheer saturation — by November 2025, the company had already labeled a staggering 1.3 billion videos as AI-generated.

On the other hand, AI companies train the models that generate AI slop on existing footage, and their output represents a flattening or aggregation of the patterns found in human-made content (rather than in “reality”) — an effect that scholar Roland Meyer has labeled “platform realism.”

Filling a new user’s experience with slop videos optimizes the feed as a familiar space and acclimatizes them to the aesthetic and thematic norms of TikTok culture.

Kids, Science and Education, and Health TikTok Categories Are Most Clogged With AI Slop

Next, we checked a sample of 10,742 TikTok videos across the most popular tags in 20 categories, noting the number of AI slop and non-AI slop videos. The category with the highest slop density by far was Kids (57.4%) — more on that below.

Science and Education (35.0%), Health (33.8%), and History (33.5%) are the nearest contenders. In the top nine categories, more than one in ten videos were AI slop. But videos in the Fitness (1.6%), Music (1.5%), and Fashion (1.3%) categories are almost entirely human-made.

Image: Kapwing In Partnership With Neomam Studios. CC BY-SA

Scientific facts and concepts lend themselves to visual illustration through animation. But when such videos are generated with haste and sensationalism as their guiding principles, the potential for deeper value is compromised.

Researchers have warned that slop educational material clutters the platform and competes with more authoritative sources.

TikToks such as the baffling Eating Lemon video below rattle along at the turbo-TikTok pace the models seem to have learned and feature the misspelled words and misshapen lettering that have come to be associated with AI imagery.

@vitalverse1 Science in action. What happens while Eating Lemon in the human body?😱🤮😱#humanbody #anatomy #3danimation #sciencetok #viral ♬ original sound - user99611117491

Meanwhile, creators such as Jonathan Laramy, the man behind Chloe VS History, see AI as a chance to bring educational topics to life, exploiting the technology rather than the viewer.

“Yes, there are people out there that obviously don't care about history,” he told Sky News, regarding his competitors, “because there are mistakes left, right, and center.”

Laramy’s content is hosted by an ultra-realistic AI presenter called Chloe, who is easily mistaken for a real person due to the “human emotion and non-verbal cues” facilitated by the latest video models.

However, the scripts are AI-generated, too, and viewers have criticized the channel for its historical inaccuracies.

Brendan Gillis, Director of Teaching and Learning at the American Historical Association, warns that AI-generated history is only as reliable as the source material it draws from. Because AI fills gaps using patterns from its training data, it can introduce inaccuracies, biases, and stereotypes.

97% of #CartoonKids TikToks Are AI Slop

Of the 2,000 featured videos we analyzed in TikTok’s Kids category, some 1,147 (57.4%) were AI slop.

The worst-affected tag, #cartoonkids, was almost entirely made up of slop, with only three of the 100 videos we checked being human-made. Around one-third or more of the videos for nearly all of the tags we checked were AI slop, and even #babytok — which had significantly less slop than other tags — featured one slop video out of every ten on its tag page. 

Image: Kapwing In Partnership With Neomam Studios. CC BY-SA

The dangers of automated content for children are not new, but the scale at which children are being targeted with slop — ranging from nonsense ‘brainrot’ to dangerously inaccurate songs and lessons — is staggering.

“I think of this as toddler AI misinformation at an industrial scale. It’s very risky for the developing brain,” says Dr. Dana Suskind, a professor of pediatrics at the University of Chicago, speaking to Mother Jones.

“Every experience is building a million new neural connections. You will be unintentionally wiring the brain in incorrect ways.”

One of the first videos to pop up under #preschoollearning appropriates Sesame Street characters to deliver a lesson about counting cookies. Not only do the numbers not match the cookies, but the animation is careless, and the voices are borderline terrifying. The comments section is stuffed with short, nonsensical sequences of letters that boost the video’s visibility.

@spongebob.uk1 Counting Cookies for Kids 🍪 Fun Learning Numbers with Cookie Monster Learn numbers in a fun way with Cookie Monster while counting delicious cookies! 🍪 Perfect for toddlers and preschool kids to practice counting, early math skills, and number recognition through a playful learning adventure. Great educational video for kids who love Sesame Street characters and fun learning! #countingcookies, #cookiemonster, #learnnumbers, #countingforkids, #kidslearning, #preschoollearning, #toddlerlearning, #numbersforkids, #learncounting, #educationalvideo, #kidseducation, #learningisfun, #funlearning, #earlylearning, #preschoolactivities, #toddleractivities, #kidsvideos, #learningnumbers, #countinggame, #numbersong, #kidsmath, #mathforkids, #kidsfunlearning, #educationalforkids, #kidsyoutube, #kidscontent, #learningvideos, #kidsteaching, #homeschoolkids, #preschoolkids, #learnwithfun, #smartkids, #educationchannel, #learningtime, #kidsstudy, #numberspractice, #countingtime, #learn123, #numbers123, #kidsnumberlearning, #funforkids, #kidslearningvideos, #playandlearn, #kidsbrain, #learningathome, #kidseducationvideos, #childeducation, #kidsedutainment, #learningnumbers123, #kidsactivityvideo, #educationfun, #learningforkids, #countingactivity, #kidsmathfun, #preschoolmath, #toddlermath, #educationalcontent, #funeducation, #kidsknowledge, #learningadventure,#animationviral #spongetherapy #underwateradventure #oceanfun #cartoonvideo #animationshorts #viralshorts #funnyanimation #cartoonforkids #seaadventure #oceanworld #cartoonfun #spongeasmr #viralvideo #youtubeviral #kidsentertainment #animatedshort #cartoonstory #oceanlife #kidsyoutube ♬ original sound - English

A recent survey from the Family Online Safety Institute found that only 51% of parents use parental controls on tablets and 47% on smartphones.

Meanwhile, over 70% of babies and under-twos use screens, and one in 10 babies regularly falls asleep with a screen, according to the UK’s 1001 Critical Days Foundation — so named because the 1,001 days from pregnancy to age two are critical for brain development, with up to one million neural connections forming every second.

The TikTok AI Slop Backlash

The people at TikTok are well aware of the AI slop backlash. In May, the company began to rein in its new AI-generated summaries, which users had noticed regularly produced bizarre mistakes, such as erroneously identifying dancer Charli D'Amelio as a “collection of various blueberries with different toppings.”

Alongside allowing users to reduce AI content in their feed, the company announced a $2 million educational fund for experts to develop content around AI literacy and safety.

But the stats reinforce the feeling that AI slop-clogged feeds are already the new normal. Meta’s Mark Zuckerberg has tagged this era of generative AI content the “third phase” of social media — following personal and then creator-based content.

This third AI phase itself has gone through different eras, from the comedic novelty of Will Smith spaghetti videos to the emotionally nuanced but factually inaccurate Chloe VS History.

But slop is likely to continue being slop, and children are sure to encounter damaging AI material as long as humans outsource the hard work of video production to robots without putting in the time and oversight to ensure higher standards are met.

This is because the training data contains mistakes. And most of all, it is because video is a mode of human communication, which is nuanced, dynamic and — well — human.

Methodology

We manually analyzed 10,742 TikTok videos across 20 categories. We began by building a seed list of 20 popular TikTok categories (e.g., Food, Travel, Fitness, etc.) and at least three of the most popular tags for each category (e.g., #traveltok and #foodie).

Next, we manually analyzed the featured videos displayed on each tag's page (e.g., https://ift.tt/K8nrFN3), recording the count of AI slop and non-AI slop videos. This allowed us to calculate the percentage of AI slop videos for each tag, which we then aggregated for each category.

To find the proportion of AI slop that is served to new users, we established a brand new TikTok account and recorded the appearance of AI slop videos in the “For You” section while scrolling the first 500 TikToks.

AI slop videos were defined as those with obvious use of AI-generated visuals, as well as low-quality clip/compilation-style videos with clearly AI-generated scripts and voiceovers.

Reviewed by Irfan Ahmad.

This article was originally published on Kapwing and republished here with permission.

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Why LinkedIn Top Voices Are Outperforming Famous CEOs (And What It Means for You)

By Cara Siera

From Microsoft’s Bill Gates to Google’s Sundar Pichai, you might expect executives who are household names to have the strongest LinkedIn profiles. But ResumeCoach’s recent analysis of 20 public profiles found that “unknown” Top Voices users often outscored famous CEOs.

Below, we’ll discuss how the analysis was conducted, why the demographics differ in profile completion, and what it means for LinkedIn users in 2026.

Profile Analysis Methodology

The sample included ten well-known C-suite executives including Gates and Pichai, and ten non-executive participants in LinkedIn Top Voices for 2026, an invitation-only program through which users are selected by LinkedIn’s editorial team for thought leadership, professionalism, and consistent use of the platform.

Each profile was reviewed using ResumeCoach’s LinkedIn Profile Analyzer, which scores profiles based on the Headline, About, Experience, Education, Licenses & certifications, Skills, and Languages section contents.

LinkedIn Rewards Complete Profiles, Not Fame

In the analysis, profiles with four or more completed sections consistently outperformed those with only one or two complete sections, regardless of their real-world status.

LinkedIn Top Voices users averaged a score of 6.7 out of 10, while the high-profile executives averaged just 3.2 out of 10. That’s a gap of 3.5 points between fame and high performance.

All Top Voices users scored well in the About section, with 90% receiving top marks for their Skills section and 70% for optimizing their Experience section.

Only two of the ten executives scored more than 5 out of 10. Seventy percent of executives neglected the Skills and Certifications sections, and 50% of scores below 3 had an empty About section.

This data suggests that LinkedIn’s algorithm may reward profile completeness rather than perceived influence, talent, impact, or real-world status. This levels the playing field, making intentional profile optimization an accessible networking tool for everyone.

Interestingly, LinkedIn’s founder, Reid Hoffman, was the highest-scoring executive with a global score of 7 out of 10. With proprietary knowledge of how LinkedIn’s algorithms function, Hoffman no doubt understands the importance of a complete profile.

Why Some CEOs Don’t Need LinkedIn

Today’s top executives built their careers through reputation, in-person networking, speaking engagements, media exposure, and courting investors. Many were well on their way before LinkedIn’s inception in 2002.

They didn’t need LinkedIn to reach the next rung of the ladder and likely relied on existing contacts for networking. While they may use LinkedIn for informational or inspirational posts, they have not fully leveraged the platform.

Chelsea Jay, a career and leadership coach whose own LinkedIn profile scored 8 out of 10 in the analysis, says, “High-profile executives do not always rely on LinkedIn as their main source of opportunity, so profile optimization can become a low priority. But LinkedIn does not only evaluate reputation. It evaluates structure, completeness, and keyword alignment.”

She adds, “It is not that executives lack skills. They are simply not translating their expertise into searchable data on the platform. For job seekers, completing sections such as About, Skills, Certifications, and Languages is one of the easiest ways to improve discoverability.”

Top Voices professionals built their audiences on LinkedIn itself. That naturally encouraged them to optimize every section of their profiles.

Why Completeness Matters More in the AI Era

As artificial intelligence (AI) becomes increasingly involved in job searches, recommendations, and recruiter tools, structured information becomes ever more valuable.

In the ResumeCoach study, the strongest profiles consistently included About, Experience, Skills, Certifications, and Education sections. This structure effectively organizes information into a format that software can more easily interpret and provides an ample supply of searchable keyword inclusion opportunities.

This, in turn, aids recruiter searches, individual networking, job recommendations, and professional discovery. The well-filled profile also appeals to human viewers, proving the profile is legitimate and the user an active member of the LinkedIn community.

Key Takeaways

If you open your LinkedIn profile today, what does it look like? Profiles with four or more completed sections consistently outperformed profiles that primarily relied on a descriptive headline. LinkedIn visibility has less to do with your professional status than with the structure and completeness of your profile.

If you’re already at the top of your industry, optimizing your LinkedIn profile may be superfluous. But fame alone doesn’t equal a strong LinkedIn presence. Professionals who don’t yet enjoy global recognition can compete for LinkedIn visibility simply by investing a little time in their profiles.

Author Bio: Cara Siera is a career and travel writer who approaches data from a background in psychology and sociology—giving cold statistics the human touch. Cara is a Certified Professional Resume Writer (CPRW), Master Beekeeper, home chef, and world traveler.

Image: Zulfugar Karimov - Unsplash

Reviewed by Irfan Ahmad.

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AI Translation and Human Interpretation Differ in Handling Contextual Meaning, Study of UN Speeches Finds

By Lingnan University

As generative artificial intelligence (AI) becomes increasingly widely used in translation, questions have been raised over whether it could eventually replace professional interpreters. A joint study led by Lingnan University found that while AI can improve translation efficiency, it is still less capable than professional interpreters of adapting language to context and preserving rhetorical and communicative effects. The researchers conclude that human judgement and oversight remain essential, particularly in politically, diplomatically, and culturally sensitive settings. These findings have been published in Humanities and Social Sciences Communications, a Nature Portfolio journal.

Image: BBC Creative - Unsplash

The research team from Lingnan University and the Chongqing University of Posts and Telecommunications analysed 16 Chinese-language speeches delivered at the United Nations General Assembly (UNGA) between 2008 and 2023, and compared the official English interpretations by professional UN conference interpreters with AI-generated translations produced by ChatGPT-4o, examining how each handled language in different contexts.

Before generating the AI translations, the researchers designed detailed prompts that included the speaker's official position, institutional background, year of delivery, audience, and broader sociopolitical stance in order to approximate the contextual information available to professional interpreters. However, despite providing the AI model with extensive contextual information, they found major differences between AI-generated translations and human interpretations in both contextual understanding and translation strategies.

One key difference concerns the use of personal pronouns. As Chinese frequently omits subjects, professional interpreters were more likely to introduce pronouns such as “our” and “they” to reflect interpersonal meanings and relationships between speakers and audiences, reinforcing collective identity and shared responsibility. AI-generated translations, by contrast, tended to produce more literal renderings with fewer personal pronouns.

For example, a Chinese sentence referring to vaccines as a powerful weapon against the pandemic was rendered by a professional interpreter as:

“Vaccination is our powerful weapon against COVID-19.”

whereas ChatGPT-4o translated it as:

“Vaccines are a powerful weapon against the pandemic.”

The researchers found that the interpreter’s addition of “our” strengthened the sense of collective identity, while the AI translation adopted a more neutral tone. The study also identified distinct differences in how obligation and responsibility were expressed. Professional interpreters were more likely to adjust modal verbs according to context, using expressions such as “should” and “need to” to convey persuasive rather than mandatory obligation. AI-generated translations, however, relied more heavily on “must” and passive constructions, making responsibility less explicit.

For example, the professional interpretation reads:

“We need to enhance coordinated global COVID-19 response and minimise the risk of cross-border virus transmission.”

whereas the AI translation states:

“International joint prevention and control must be strengthened, and the cross-border spread of the virus must be minimised.”

The researchers found that the AI version obscures the agent responsible for action by using passive constructions.

The study also examined culturally embedded metaphors. More than half (52.63 per cent) of the AI translations reduced culturally specific metaphors to their literal meanings, weakening their rhetorical force. By contrast, professional interpreters adopted more flexible strategies, preserving, adapting, and explaining metaphorical expressions according to context. In about one-third of the cases (31.6 per cent), interpreters retained the metaphor and also conveyed its intended meaning.

One example involved the traditional Chinese metaphor of people travelling “in the same boat”. The professional interpreter translated it as:

“We are called upon by our times to unite as one and work together for mutual benefit and win-win progress like passengers in the same boat.”

While ChatGPT-4o rendered it as “Working together and achieving mutual benefits and win-win outcomes are the objective demands of our time.”

According to the researchers, the AI translation conveyed the general meaning, but omitted the metaphorical imagery and its rhetorical impact.

The team noted that ChatGPT-4o generally produces fluent and grammatically accurate translations capable of completing translation tasks effectively. However, drawing on socio-cognitive theory, the study argues that professional interpreters consider not only the source text itself but also factors such as the speaker's identity, communicative setting, audience, cultural background, stance, and rhetorical purpose when deciding how to translate. This suggests that current large language models have yet to replicate fully the human capacity to interpret context and cultural meaning.

Prof Wang Binhua, Professor of the Department of Translation and Head of the Centre for English and Additional Languages at Lingnan University, member of the SIG on Artificial Intelligence in Translation and Interpreting of the European Language Council (ELC), said “Large language models still face the challenge of the ‘black box’, meaning that the mechanisms through which they produce particular translations remain difficult to explain. Unlike professional interpreters, who work within established professional ethical standards and are accountable, AI systems generate translations by identifying patterns in large volumes of language data and do not possess an intrinsic ethical framework. In translation tasks that require careful attention to cultural meaning and contextual understanding, human interpreters remain indispensable in making informed judgements about interpersonal relationships, rhetorical choices, and cultural expression.”

He added that AI is better positioned to augment rather than replace professional translators and interpreters. When integrated with human expertise, AI has the potential to improve efficiency while leaving context-sensitive and culturally informed decision-making in human hands.

For the full research paper A tale of two ‘contexts’: ideological differences in the translations of UN political speeches by human interpreters and by ChatGPT4o, please visit: https://www.nature.com/articles/s41599-026-07877-7.

This article was originally published by Lingnan University and republished here with permission.

Reviewed by Irfan Ahmad.

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